Short answer: Life sciences cloud workloads differ from typical business workloads in three ways: data volume; high-performance compute that comes in bursts; and regulatory requirements attached to the data itself. A single human genome takes up about 200 GB, and one cryo-EM session can produce 1 to 2 TB a day. Across our clients, that adds up to workloads 10 to 20 times heavier than other industries run. That data also has to stay traceable under GxP and 21 CFR Part 11.
Most cloud environments are designed around business applications: email, CRM, file sharing, a few databases. Those workloads are steady, predictable, and mostly small. A biotech’s cloud environment runs a different kind of job entirely. It moves raw instrument output measured in terabytes, runs pipelines that need hundreds of cores for a few hours and then nothing, and stores results that regulators may one day ask to trace back to the instrument that created them.
What makes a life sciences cloud workload “heavy”?
A life sciences cloud workload is heavy because of the raw data scientific instruments produce and the compute needed to turn that data into results. Sequencers, cryo-electron microscopes, and high-content imaging systems each routinely generate terabytes. Analysis pipelines then need large bursts of CPU, GPU, and memory to process that data.
For scale, NHGRI estimates that data about a single human genome sequence takes up about 200 GB (NHGRI Genomic Data Science Fact Sheet, updated 2022). A typical business application might store 200 GB across its entire user base.
How much data do scientific instruments actually generate?
Modern scientific instruments generate terabytes per run or per day. That scale turns storage, transfer, and processing into core infrastructure decisions rather than afterthoughts.
Specific examples:
- Sequencing: a single NovaSeq X Plus 25B flow cell produces roughly 8 to 10.5 Tb of sequence data per run at 2 × 150 bp, enough for about 64 human genomes at 30x coverage (Illumina NovaSeq X specifications).
- Cryo-EM: a single cryo-TEM experiment can generate more than 1 to 2 TB of data per day, and some facilities produce between 500 TB and 2 PB a year (Histochemistry and Cell Biology, 2023).
- High-content imaging and lab automation: these systems produce a steady stream of large image files that need automated movement from the instrument to cloud storage.
Why does compute demand spike instead of staying steady?
Scientific compute demand spikes because analysis runs in batches tied to experiments. A sequencing run finishes, a pipeline kicks off, hundreds of cores run for hours, and then demand drops close to zero until the next run.
That pattern suits the cloud well, but only when the environment scales up and down automatically. A cloud environment sized for peak demand wastes money most of the month. One sized for average demand leaves scientists waiting in a queue. Getting this right takes elastic compute, spot or reserved capacity where it fits, and job scheduling designed around how the lab actually works.
How does compliance change the way life sciences data is stored?
In life sciences, compliance attaches to the data itself, not just the application. Once clinical or manufacturing data lives in electronic systems, 21 CFR Part 11 and GxP require audit trails, access controls, and proof that records haven’t been altered outside logged, authorized processes.
That changes storage design. A life sciences cloud environment needs immutable storage options, logged access, documented retention policies, and a clear chain of custody from instrument to analysis to submission. In a typical business cloud, compliance mostly means encryption and backups. In a GxP environment, the infrastructure has to prove its own integrity.
Why do generic cloud setups struggle with scientific workloads?
Generic cloud setups struggle because they’re built around steady business traffic and small files. Scientific workloads bring terabyte-scale datasets, compute that bursts and then drops to zero, and regulated records. Each of those breaks a different assumption in a standard configuration.
The failures tend to look the same: storage costs climb because everything sits in the most expensive tier; pipelines run slowly because compute isn’t sized for the job; data transfer fees surprise finance at month-end; and nobody can show an auditor how a result traces back to the raw file. None of these is a cloud problem. Each one is a design problem.
What does a cloud environment built for life sciences look like?
A cloud environment built for life sciences combines tiered storage for massive datasets, elastic compute for burst analysis, automated instrument-to-cloud data movement, and compliance controls built in from the start. It’s designed around how the lab generates and uses data, not around a standard IT template.
In practice, that means hot storage for active analysis and automatic lifecycle rules that move older data to cheaper archive tiers. It means compute that scales to zero between runs, and pipelines orchestrated so scientists don’t have to manage infrastructure themselves. It also means audit logging and access controls that satisfy Part 11 without slowing anyone down.
How much does it cost when a scientific workload runs on the wrong architecture?
A poorly matched architecture costs money in wasted spend, lost scientist time, and rework before an audit. The waste problem isn’t unique to life sciences, but heavy workloads make it bigger. Organizations estimate that 29% of their IaaS and PaaS spend is wasted, the first increase in five years (Flexera 2026 State of the Cloud Report, March 2026).
When a workload is 10 to 20 times heavier, the same percentage of waste becomes a far larger dollar amount. The right architecture also moves faster: in one PTP engagement, re-architecting an AlphaFold workload on AWS made processing 12 times faster and cut costs by 86%.
Business Workloads vs. Life Sciences Workloads
| Characteristic | Typical business workload | Life sciences workload |
|---|---|---|
| Data per unit of work | Megabytes to gigabytes | ~200 GB per genome; 1 to 2 TB per cryo-EM day |
| Compute pattern | Steady, predictable | Bursty: hundreds of cores for hours, then idle |
| Data sources | Users and applications | Sequencers, microscopes, imaging, lab automation |
| Compliance scope | Encryption, backups, access | GxP, 21 CFR Part 11, audit trails, chain of custody |
| Storage strategy | Single tier often works | Tiered hot, cool, and archive with lifecycle rules |
| Biggest cost risk | License and seat sprawl | Storage tiering, idle compute, and data transfer |
What do biotechs get wrong when moving scientific workloads to the cloud?
The most common mistake is lifting and shifting scientific workloads into a cloud environment designed for business applications. The workloads technically run, but they cost too much and move too slowly. A close second is leaving every dataset in hot storage indefinitely, which is often the single largest line item on a biotech’s cloud bill.
Biotechs also underestimate data transfer. Moving terabytes between regions, between clouds, or back to on-premises systems adds fees that rarely appear in the original estimate. A third mistake is treating compliance as a later phase. Retrofitting audit trails and access controls onto a scientific data platform already in use is slower and riskier than building those controls in from the start.
The data behind life sciences cloud workloads
Data per genome: data about a single human genome sequence takes up about 200 GB, according to the National Human Genome Research Institute (NHGRI, 2022).
Instrument output: a single cryo-TEM experiment can generate more than 1 to 2 TB of data per day, and some microscopy facilities produce 500 TB to 2 PB a year (Histochemistry and Cell Biology, 2023).
Cloud waste: organizations estimate 29% of IaaS and PaaS spend is wasted, up for the first time in five years (Flexera 2026 State of the Cloud Report, March 2026).
FAQ
Can a small preclinical biotech have “heavy” cloud workloads?
Yes. Workload weight depends on the science, not the headcount. A 15-person company running whole-genome sequencing or cryo-EM can generate more data than a 500-person company in another industry. Plan the architecture around the instruments and pipelines in use, not around company size.
Is the cloud cheaper than on-premises for scientific computing?
It can be, especially for bursty workloads that would leave on-premises hardware idle most of the time. The savings depend on architecture: storage tiering, compute that scales to zero, and controlled data transfer. A poorly designed cloud environment can cost more than the servers it replaced.
Which cloud services matter most for life sciences workloads?
Object storage with lifecycle tiering, elastic and GPU compute, workflow orchestration for pipelines, and purpose-built services such as AWS HealthOmics for genomics data. The right mix depends on the modalities a lab runs: sequencing, imaging, structural biology, or all three.
How early should a biotech plan its scientific cloud architecture?
Before the first instrument connects to the cloud, if possible. Architecture decisions made in the first year, such as storage layout, account structure and data movement, are much harder to change once terabytes of data and validated workflows depend on them.
Related reading
- Why Life Sciences Needs a Purpose-Built IT Partner (Not Just Another MSP)
- How Should a Biotech Architect Cloud Infrastructure for Genomics, Imaging, and Big Data?
- What Does an AWS Partnership Actually Deliver for a Biotech’s Cloud Infrastructure?
- What IT Infrastructure Does a Biotech Need Before Entering the Clinic?
- Ask the Architect: What a Life Sciences Cloud Stack Should Look Like on Day One
Wondering whether your cloud environment was built for your science? Get a cloud workload review → Contact Us.